Aidos Ishmanov

Name: Aidos Ishmanov Affiliation: Postgraduate. International IT University, Almaty, Kazakhstan Address: Dzandosov/Manasa st, 34/8a, Almaty, Kazakhstan Brief Biographical History: 2009 — Graduated from №165 Technical Lyceum, Almaty, Kazakhstan 2013 — Bachelor of Computer Science, International IT University, Almaty, Kazakhstan 2015 — Master’s degree in Computer Science, International IT University, Almaty, Kazakhstan Main Works: Ishmanov A.O., Alikhojayev I.B. Spatial data consolidation for decision support in the…

Alim N. Khamitov

CV in English Name: Khamitov Alim Nadimovich Affiliation: Ph.D Address: Dzandosov/Manasa st, 34/8a, Almaty, Kazakhstan Brief Biographical History: 2005-2009 — Bachelor degree of Information Systems at Indiana University, Bloomington, USA 2006 — IT Manager at the Institute of aerocosmic research, Kazakhstan 2008 — Sales Manager at Sulpak LLP, Kazakhstan 2009 — Senior Lecturer at Information Systems Department, International Information Technology University, Almaty, Kazakhstan 2011 —…

Revelation of new ICT domains for upcoming Kazakhstan’s participation

Ravil I. Muhamedyev, Jelena Muhamedyeva, Yedilkhan N. Amirgaliyev, Alim N. Khamitov, Ainur Abdilmanova. Revelation of new ICT domains for upcoming Kazakhstan’s participation // Proceedings of the 2015 Conference on Electronic Governance and Open Society: Challenges in Eurasia, EGOSE-2015. —St.Petersburg.ACM New York. — 2015, — P. 179-188. ABSTRACT Rapid changes in ICT affect the field of communication, information processing and the devices that collect and process the data. It seems…

Use of machine learning: computational methods, quality rating, data pre-processing

R. Muhamedyev.  Machine learning methods: An overview // Computer Modeling and New Technologies. Scientific and research journal. – Riga, 2015. — 19(6). — P. 14-29. Abstract This review covers the vast field of machine learning (ML), and relates to weak artificial intelligence. It includes the setup diagram of machine learning methods, the formal statement of ML and some frequently used algorithms (regressive, artificial neural networks, k-NN,…

Таксономия методов машинного обучения и оценка качества классификации и обучаемости

Мухамедиев Р. И., Мухамедиева Е.Л., Кучин Я. И. Таксономия  методов  машинного обучения и оценка качества классификации и обучаемости // Электронный журнал Cloud of science. – 2015. – T. 2, № 3 – 13 c. Аннотация В работе рассматриваются так называемые интеллектуальные методы и как их важная составляющая — сфера машинного обучения (machine learning — ML), относящаяся к части слабого искусственно интеллекта. Приведена таксономия методов  ML…

Premises for the creation of renewable energy sources GIS monitoring

Ravil I. Muhamedyev, Andrey D. Giyenko, Victor T. Pyagai, Kairat Bostanbekov. Premises for the creation of renewable energy sources GIS monitoring // Proceedings of 8th IEEE International Conference on Application of Information and Communication Technologies — AICT2014. – Astana, 2014. – 398-402 Abstract Control of the status of the country’s resourcesallows making reasonable decisions both in the field of state regulation, and for the benefit of…

Technological preconditions for monitoring renewable energy

Muhamedyev R.I., Muhamedyeva E. Technological preconditions for monitoring renewable energy // The 13th International conference information technologies and management. – Riga: Information Systems Management Institute, 2015. – P. 112-115. Abstract Wireless sensor networks, inter-machine communication system (Machine-to-Machine — M2M) and broadband networks based on new communication protocols that provide high speed and reliability of inter-machine connections will become the technological basis for big scale monitoring. Combining such…

Application of machine learning to the monitoring of renewable energy sources

Muhamedyev, K. Yakunin. Application of machine learning to the monitoring of renewable energy sources // The 13th International conference information technologies and management. – Riga: Information Systems Management Institute, 2015. – P. 126-127. [http://geoml.info/?p=336] Abstract The paper covers opportunities for application of Machine Learning and other  mathematical/statistical models and artificial intelligence algorithms in a geographic information system developed for monitoring of renewable energy sources. The system…